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Richard J. Samworth
Researcher at University of Cambridge
Publications - 126
Citations - 6372
Richard J. Samworth is an academic researcher from University of Cambridge. The author has contributed to research in topics: Estimator & Minimax. The author has an hindex of 36, co-authored 120 publications receiving 5209 citations. Previous affiliations of Richard J. Samworth include University of Washington & University of Wollongong.
Papers
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Journal ArticleDOI
Screening of healthcare workers for SARS-CoV-2 highlights the role of asymptomatic carriage in COVID-19 transmission.
Lucy Rivett,Lucy Rivett,Sushmita Sridhar,Sushmita Sridhar,Dominic Sparkes,Dominic Sparkes,Matthew Routledge,Matthew Routledge,Nick K Jones,Sally Forrest,Jamie Young,Joana Pereira-Dias,William L Hamilton,William L Hamilton,Mark Ferris,M. Estée Török,Luke W. Meredith,Martin D. Curran,Stewart Fuller,Afzal N. Chaudhry,Ashley Shaw,Richard J. Samworth,John Bradley,John Bradley,Gordon Dougan,Kenneth G. C. Smith,Paul J. Lehner,Nicholas J Matheson,Giles Wright,Ian Goodfellow,Stephen Baker,Michael P. Weekes +31 more
TL;DR: The utility of comprehensive screening of HCWs with minimal or no symptoms for SARS-CoV-2 testing is demonstrated, and this approach will be critical for protecting patients and hospital staff.
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A useful variant of the Davis--Kahan theorem for statisticians
TL;DR: In this paper, the authors present a variant of the Davis-Kahan theorem that relies only on a population eigenvalue separation condition, making it more natural and convenient for direct application in statistical contexts, and provide an improvement in many cases to the usual bound.
Journal Article
Ultrahigh Dimensional Feature Selection: Beyond The Linear Model
TL;DR: This paper extends ISIS, without explicit definition of residuals, to a general pseudo-likelihood framework, which includes generalized linear models as a special case and improves ISIS by allowing feature deletion in the iterative process.
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Choice of neighbor order in nearest-neighbor classification
TL;DR: In this paper, the authors consider two models, Poisson and Binomial, for the training samples, and show that the risk of misclassification is asymptotically equivalent to first order.
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Variable selection with error control: another look at stability selection
TL;DR: In this article, a variant of stability selection, called complementary pairs stability selection (CPSS), is introduced, and bounds are derived on the expected number of variables included by CPSS that have low selection probability under the original procedure.